Value creation in emerging technologies through text mining: the case of blockchain

Abstract

As technology progresses, organisations must understand where to direct their value-creating efforts to achieve or sustain competitive advantage. This is even more true in the case of emerging technologies, where innovative activities often focus on achieving a technology promise while overlooking a set of technological, operational, organisational and user-related problems that must be overcome before the technology can fulfil this promise. Through an innovative application of textmining, this paper develops a practical methodology to identify a range of problems related to a technological field in an unsupervised manner, that may benefit firms, researchers and policymakers. We apply the methodology to the field of blockchain and compare it to traditional literature reviews.

Twenty years of gender equality research: A scoping review based on a new semantic indicator

“…We used this information considering that abstracts, titles and keywords represent the most informative part of a paper, while using the full-text would increase the signal-to-noise ratio for information extraction. Indeed, these textual elements already demonstrated to be reliable sources of information for the task of domain lexicon extraction [ 43 , 44 ]. We chose Scopus as source of literature because of its popularity, its update rate, and because it offers an API to ease the querying process.…”

“…Specifically, we only retained those papers mentioning, in their title and/or abstract, both gender-related keywords (e.g., daughter, female, mother) and keywords referring to bias and equality issues (e.g., equality, bias, diversity, inclusion). After text pre-processing (see next section), keywords were first identified from a frequency-weighted list of words found in the titles, abstracts and keywords in the initial list of papers, extracted through text mining (following the same approach as [ 43 ]). They were selected by two of the co-authors independently, following respectively a bottom up and a top-down approach.…”

“…However, we observed a shift in the more recent years towards solution seeking in this domain, which we strongly encourage future researchers to focus on. In the future, we may focus on collecting and mapping pro-active contributions to gender studies, using additional Natural Language Processing techniques, able to measure the sentiment of scientific papers [ 43 ].…”

A Review of Digital Transformation on Supply Chain Process Management Using Text Mining

“…Dallasega et al (2018) [19] presented a framework for illustrating Industry 4.0 concepts in construction SCs. Müller and Voigt (2018) [20] examined the subject of SCM in the field of Industry 4.0 and provided challenges, potentials, and recommendations for their integration. Ghadimi et al (2019b) [21] investigated the stable supplier evaluation and selection process for SCs in Industry 4.0.…”